WPS4305
Policy ReseaRch WoRking PaPeR 4305
A Ricardian Analysis of the Impact
of Climate Change on African Cropland
Pradeep Kurukulasuriya
Robert Mendelsohn
The World Bank
Development Research Group
Sustainable Rural and Urban Development Team
August 2007
Policy ReseaRch WoRking PaPeR 4305
Abstract
This study examines the impact of climate change on study also examined some simple climate scenarios to
cropland in Africa. It is based on a survey of more than see how Africa would respond to climate change. These
9,000 farmers in 11 countries: Burkina Faso, Cameroon, uniform scenarios assume that only one aspect of climate
Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South changes and the change is uniform across all of Africa.
Africa, Zambia, and Zimbabwe. The study uses a In addition, the study examined three climate change
Ricardian cross-sectional approach in which net revenue scenarios from Atmospheric Oceanic General Circulation
is regressed on climate, water flow, soil, and economic Models. These scenarios predicted changes in climate in
variables. The results show that net revenues fall as each country over time.
precipitation falls or as temperatures warm across all the Not all countries are equally vulnerable to climate
surveyed farms. change. First, the climate scenarios predict different
In addition to examining all farms together, the study temperature and precipitation changes in each country.
examined dryland and irrigated farms separately. Dryland Second, it is also important whether a country is already
farms are especially climate sensitive. Irrigated farms hot and dry. Third, the extent to which farms are
have a positive immediate response to warming because irrigated is also important.
they are located in relatively cool parts of Africa. The
This paper--a product of the Sustainable Rural and Urban Development Team, Development Research Group--is part
of a larger effort in the group to mainstream climate change research. Policy Research Working Papers are also posted on
the Web at http://econ.worldbank.org. The author may be contacted at robert.mendelsohn@yale.edu.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
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A RICARDIAN ANALYSIS OF THE IMPACT
OF CLIMATE CHANGE ON AFRICAN CROPLAND1
Pradeep Kurukulasuriya and Robert Mendelsohn2
1An earlier version of this Working Paper was published as CEEPA Discussion Paper number 8.
2 School of Forestry and Environmental Studies, Yale University, 230 Prospect St, New Haven, CT 06511,
USA. E-mails: pradeep.kurukulasuriya@yale.edu; robert.mendelsohn@yale.edu. The authors wish to thank
Rashid Hassan, David Maddison and Ariel Dinar for their comments. The special effort of Jeffrey Lecksel in
fixing some of the maps in this paper are much appreciated.
This paper was funded by the GEF and the World Bank. It is part of a larger study on the effect of climate
change on agriculture in Africa, managed by the World Bank and coordinated by the Centre for Environmental
Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa.
1
SUMMARY
This study examines the impact of climate change on cropland in Africa. It is based on an 11-
country survey of over 9000 farmers administered as part of a Global Environment Facility
(GEF) project. Five of the countries are West African: Burkina Faso, Cameroon, Ghana,
Niger and Senegal; three are from Southern Africa: South Africa, Zambia and Zimbabwe;
two are East African: Ethiopia and Kenya; and Egypt is the sole representative of North
Africa. The study uses a Ricardian cross-sectional approach to measure the relationship
between the net revenue from growing crops and climate. Net revenue is regressed on
climate, water flow, soils and economic variables. The resulting regression explains the role
that each variable plays today. We find that net revenues fall as precipitation falls or as
temperatures warm across all the surveyed farms. Specifically, the elasticity of net revenue
with respect to temperature is -1.3. This elasticity implies that a 10% increase in temperature
would lead to a 13% decline in net revenue. The elasticity of net revenue with respect to
precipitation is 0.4.
In addition to examining all farms together, the study examined dryland and irrigated farms
separately. Dryland farms are especially climate sensitive. The elasticity of net revenue with
respect to temperature is -1.6 for dryland farms but 0.5 for irrigated farms. Irrigated farms
have a positive immediate response to warming because they are located in relatively cool
parts of Africa. The elasticity of net revenue with respect to precipitation is 0.5 for dryland
farms but only 0.1 for irrigated farms. Irrigation allows farms to operate in areas with little
precipitation, such as Egypt.
The study also examined some simple climate scenarios to see how Africa would respond to
climate change. These `uniform' scenarios assume that only one aspect of climate changes
and the change is uniform across all of Africa. For example, the study examined a 2.5°C
warming and found that net revenues from farming in all of Africa would fall by $23 billion.
It also examined a 5°C warming and found that this would cause net revenues to fall $38
billion. A 7% decrease in precipitation would cause net revenues from crops to fall $4 billion
and a 14% decrease in precipitation would cause it to fall $9 billion. Increases in precipitation
would have the opposite effect on net revenues.
In addition to the uniform scenarios, the study also examined three climate change scenarios
from Atmospheric Oceanic General Circulation Models (AOGCMs). These AOGCM
scenarios predicted changes in climate in each country over time. They reveal that African
net revenues may rise by up to $97 billion if future warming is mild and wet but would fall
by up to $48 billion if future climates are hot and dry. Dryland farms would be affected the
most by either beneficial or harmful scenarios. Irrigated farms are relatively resilient to
climate change.
Not all countries are equally vulnerable to climate change. First, the climate scenarios predict
different temperature and precipitation changes in each country. Second, it is also important
whether a country is already hot and dry. Any increase in temperature or reduction in
precipitation in these countries leads to large impacts per farm. Third, the extent to which
farms are irrigated is also important. Dryland farmers in Africa have little recourse if the
climate becomes more hostile.
2
TABLE OF CONTENTS
Section Page
1 Introduction 4
2 Theory 5
3 Data and empirical analyses 9
4 Forecasts of climate impacts 15
5 Conclusion and implications for policy 19
References 22
3
1. Introduction
The greatest damages from climate change are predicted to be in the agricultural sector in
Sub-Saharan Africa. Agriculture is predicted to be especially vulnerable in this region
because it already endures high heat and low precipitation, is a large fraction of the economy,
and relies on relatively basic technologies (Pearce et al. 1996; McCarthy et al. 2001). Despite
this dire prediction, relatively few economic studies have tried to quantify the damages to
African agriculture using African data (Kurukulasuriya & Rosenthal 2003). What little
information there is available from agronomic studies (such as Rosenzweig & Parry 1994)
suggests that warming would have large effects. Other notable exceptions include some
economic analyses of specific crops in regions within selected countries (Molua 2002;
Gbetibouo & Hassan 2005; Deressa et al. 2005) and limited agronomic studies (such as
Rosenzweig & Parry 1994). These studies suggest that warming would have large effects.
But they do not have the scope of this study and cannot completely capture how farmers
might respond to warming and thus what the overall economic impacts might be.
This study is based on a cooperative research effort among 11 African countries: Burkina
Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zambia, and
Zimbabwe. Its purpose is to understand how climate affects current African farmers. Using
empirical data about current farmers, the study intends to predict how climate change will be
likely to affect future farmers in Africa. The sample of farmers was distributed across many
different climate zones so that there would be a great deal of climate variation among the
participants. After data cleaning, 9064 surveys of individual farmers were useable (see Table
1).
This study uses the Ricardian method to measure how climate affects net revenues. This
method is a cross-sectional technique that measures what determines net revenues to farmers
(Mendelsohn et al. 1994; Mendelsohn & Nordhaus 1996; Mendelsohn & Dinar 2003). It has
been applied to selected countries in the low latitudes, namely Brazil and India (Sanghi 1998;
Mendelsohn et al. 2001), using district level data, and Sri Lanka and Cameroon
(Kurukulasuriya & Ajwad 2006; Molua 2002), using household level data, but never across a
continent using household level data.
Section 2 briefly reviews the theory behind the Ricardian method and discusses its potential
advantages and disadvantages and the empirical specification. Section 3 then discusses the
4
results for Africa and examines regression models for all farms in Africa, dryland farms, and
irrigated farms. Section 4 examines the implications of these empirical results given a set of
uniform climate change scenarios and future climate scenarios based on climate models
(AOGCMs). The paper concludes with a summary and general policy implications.
2. Theory
The Ricardian method is a cross-sectional approach to studying agricultural production. It
was named after David Ricardo (1772­1823) because of his original observation that the
value of land would reflect its net productivity. Farmland net revenues (V) reflect net
productivity. This principle is captured in the following equation:
V = Pi Qi (X, F, H, Z, G) - Px X (1)
where Pi is the market price of crop i, Qi is the output of crop i, X is a vector of purchased
inputs (other than land), F is a vector of climate variables, H is water flow, Z is a set of soil
variables, G is a set of economic variables such as market access and Px is a vector of input
prices (see Mendelsohn et al. 1994). The farmer is assumed to choose X to maximize net
revenues given the characteristics of the farm and market prices. The Ricardian model is a
reduced form model that examines how several exogenous variables, F, H, Z and G, affect
farm value.
The standard Ricardian model relies on a quadratic formulation of climate:
V = 0 + 1F + 2 F2 +3H +4 Z + 5 G + u (2)
where u is an error term. Both a linear and a quadratic term for temperature and precipitation
are introduced. The expected marginal impact of a single climate variable on farm net
revenue evaluated at the mean is:
E[dV/dfi]= b1,i + 2*b2,i *E[fi] (3)
5
The quadratic term reflects the nonlinear shape of the net revenue of the climate response
function (Equation 2). When the quadratic term is positive, the net revenue function is U-
shaped and when the quadratic term is negative, as in Figure 1, the function is hill-shaped.
We expect, based on agronomic research and previous cross-sectional analyses, that farm
value will have a hill-shaped relationship with temperature. For each crop there is a known
temperature at which that crop grows best across the seasons. Crops consistently exhibit a
hill-shaped relationship with annual temperature, although the peak of that hill varies with
each crop. The relationship of seasonal climate variables, however, is more complex and may
include a mixture of positive and negative coefficients across seasons.
The change in welfare, U, resulting from a climate change from C0 to C1 can be measured
as follows.
U =V(C1)-V(C0) (4)
If the change increases net income it will be beneficial and if it decreases net income it will
be harmful.
Cross-sectional observations across different climates can reveal the climate sensitivity of
farms. The advantage of this empirical approach is that the method not only includes the
direct effect of climate on productivity but also the adaptation response by farmers to local
climate. This farmer behavior is important because it mitigates the problems associated with
less than optimal environmental conditions. Analyses that do not include efficient adaptation
(such as the early agronomic studies) overestimate the damages associated with any deviation
from the optimum. Adaptation thus explains both the more optimistic results found with the
Ricardian method and the generally pessimistic results found with purely agronomic studies.
Adaptation is clearly costly. The Ricardian model takes into account the costs of different
alternatives. For example, if a farmer decides to introduce a new crop on his land as climate
warms, the Ricardian model assumes the farmer will pay the costs normally associated with
growing that new crop. That is, the farmer will have to pay for new seeds and new equipment
specific to the crop. The Ricardian model does not, however, measure transition costs. For
6
example, if a farmer has crop failures for a year or two as he learns about a new crop, this
transition cost is not reflected in the analysis. Similarly, if the farmer makes the decision to
move to a new crop suddenly, the model does not capture the cost of decommissioning
capital equipment prematurely. Transition costs are clearly very important in sectors where
there is extensive capital that cannot easily be changed. For example, studies of timber
(Sohngen et al. 2002) show that modeling the transition is absolutely necessary in order to
reflect how difficult it is to change the forest stock. Although agriculture adapts quickly to
changes in prices, many intertemporal agricultural studies argue that farms will have more
difficulty adapting quickly to climate change (Kaiser et al. 1993a,b; Kelly et al. 2005). Given
how slowly some innovations in modern agriculture have spread in Africa in particular,
transition costs may be very important.
Another drawback of the Ricardian approach is that it cannot measure the effect of variables
that do not vary across space. Specifically, this approach cannot detect the effect of different
levels of carbon dioxide since carbon dioxide levels are generally the same across the world.
Changes in carbon dioxide levels have occurred over recent decades. In principle, one might
be able to detect the effect of these increases in CO2 by looking at productivity over time.
However, it is impossible to distinguish the effect of the carbon dioxide changes from the
much larger effect of technical changes that have occurred across the same time period
(Mendelsohn 2005). The best evidence about the magnitude of the fertilization effects of
carbon dioxide comes from controlled experiments. These studies report an almost universal
fertilization effect for all crops, although the magnitude of this effect varies from crop to crop
(Reilly et al. 1996). Reilly reports an average improvement in productivity of 30% associated
with a doubling in CO2. However, these results must be interpreted cautiously because the
conditions in the controlled experiments may not be representative of farms across the world.
In most cases, the laboratory experiments have been done in near ideal conditions where
other nutrients are freely available. In practice, if nutrients are scarce, the fertilization benefits
from increased carbon dioxide levels may be lower. Thus in many developing countries,
where fertilizers are not fully applied, the actual carbon fertilization benefits may be less than
30%.
Another potential drawback is that the variation in climate that one could observe across
space may not resemble the change in climate that will happen over time. For example, the
temperature range across space could be small relative to the change in temperature over the
7
next century. This explains why one may not be able to estimate a Ricardian model in small
countries. If the range of climates in a country is small, one cannot detect how climate might
affect crops. This specific problem does not apply to this study as there is a wide range of
climate variation across the sample. However, it may still be true that climates in the future
will not resemble any existing climates. For example, the climate could become erratic,
leading to precipitation events that are simply not common today. The analysis cannot
measure the impact of such changes.
The Ricardian model also assumes that prices remain constant. As argued by Cline (1996),
this introduces a bias in the analysis, overestimating benefits and underestimating damages.
The Ricardian approach, by relying on a cross section, cannot adequately control for prices
since all farms in the same country effectively face the same prices. However, calculating
price changes is not a straightforward task, since prices are a function of the global market.
Studies that have claimed to take price changes into account have had to make gross
assumptions about how world output would change with climate change. These global
assumptions also may introduce bias if they are not correct. Further, even analysts who have
assumed large agronomic impacts from global warming predict that greenhouse gases would
have only a small net effect on aggregate global food supply (Reilly et al. 1996). If aggregate
supplies do not change a great deal, the bias introduced by the Ricardian assumption of
constant prices is likely to be small (Mendelsohn & Nordhaus 1996). If the supplies of some
commodities increased and others decreased, welfare effects would offset each other. In this
case the bias could be large relative to the remaining small net effect. However, even in this
case the absolute size of the bias would remain small. In a separate analysis, Kumar and
Parikh (2001) include prices in their interannual analysis of Indian agriculture. The inclusion
of the price terms appears to have little impact on the climate coefficients.
Another valid criticism that has been leveled against the Ricardian analysis concerns the
absence of explicit inclusion of irrigation. Cline (1996) and Darwin (1999) both argued that
irrigation should be explicitly included in the analysis. This problem has been addressed in
the literature by explicitly modeling irrigation (Mendelsohn & Nordhaus 1999; Mendelsohn
& Dinar 2003). This study explicitly includes irrigation and also includes measures of flow
and runoff.
A final concern about the Ricardian method is that it reflects current agricultural policies. If
countries subsidize specific inputs or regulate crops, these policies will affect farmer choices.
8
The Ricardian results will consequently have these distortions embedded in the results. For
example, if a country mandates that a fraction of cropland be devoted to a certain crop, one
may well see more of that crop in that country than elsewhere. We can control for such
effects using country dummies. In general, we prefer not to place dummies unless there is
evidence of a distortion. Nonetheless, if future decision makers eliminate these subsidies or
introduce new ones, the empirical results may no longer hold. Policies that differ across
countries could contribute to some of the differences in farm net revenue.
3. Data and empirical analyses
The data for this study were collected by national teams. In each country, districts were
chosen to get a wide representation of farms across climate conditions in that country. The
districts were not representative of the distribution of farms in each country as there are more
farms in more productive locations. In each chosen district, a survey was conducted of
randomly selected farms. The sampling was clustered in villages to reduce sampling costs.
A total of 9597 surveys were administered across the 11 countries in the study. The number
of surveys varied from country to country. (For more information on the survey method and
the data collected see Dinar et al. 2006.) Not all the surveys could be used. Some farms did
not grow crops (they only raised livestock). Some surveys contained incorrect information
about the size of the farm, cropping area or some of the farm operating costs. Impossible
values were treated as missing values. It is not clear what the sources of these errors were but
field and measurement errors are most likely. They may reflect a misunderstanding of the
units of measurement, they may reflect a language barrier, or they may be intentional
incorrect answers. The final number of useable surveys is 9064 and their distribution by
country is shown in Table 1.
Data on climate were gathered from two sources. We relied on temperature data from
satellites operated by the Department of Defense (Basist et al. 2001). The Defense
Department uses a set of polar orbiting satellites that pass above each location on earth
between 6am and 6pm every day. These satellites are equipped with sensors that measure
surface temperature by detecting microwaves that pass through clouds (Weng & Grody
1998). The precipitation data come from the Africa Rainfall and Temperature Evaluation
System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and
Atmospheric Association's Climate Prediction Center, is based on ground station
measurements of precipitation. The temperatures for each country in the sample are shown in
9
Table 2 and the precipitation data in Table 3. Note that there is a wide range of climates
across the 11 countries in the sample.
It is not self-evident how to represent monthly temperatures and precipitation data in a
Ricardian regression model. The correlation between adjacent months is too high to include
every month. We explored several ways of defining three-month average seasons. Comparing
the results, we found that defining winter in the northern hemisphere as the average of
November, December and January provided the most robust results for Africa. This
assumption in turn implies that the next three months would be spring, the three months after
that would be summer, and August, September and October would be fall (in the north).
These seasonal definitions were chosen because they provided the best fit with the data and
reflected the mid-point for key rainy seasons in the sample. We adjusted for the fact that
seasons in the southern and northern hemispheres occur at exactly the opposite months of the
year. We also explored defining seasons by the coldest month, the month with highest
rainfall, and solar position, but found these definitions did a poorer job of explaining current
agricultural performance.
Soil data were obtained from FAO (2003). The FAO data provide information about the
major and minor soils in each location as well as slope and texture. Data concerning the
hydrology were obtained from the University of Colorado (Strzepek & McCluskey 2006).
Using a hydrological model for Africa, the hydrology team calculated flow and runoff for
each district in the surveyed countries. Figure 1 depicts the distribution of estimated long run
flow (in m3) across the continent. Data on elevation at the centroid of each district were
obtained from the United States Geological Survey (USGS 2004). The USGS data are
derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds
(approximately one kilometer).
The literature has made it clear that irrigation and water availability is an important variable
in crop production. Irrigated land is generally considered to be of the highest value. However,
in Africa most agricultural areas rely on rain (nearly 80%). We explore in this analysis the
effect of irrigation on the climate response functions of farmers in different regions of Africa.
The irrigation variable is based on plot specific data on water sources. If any primary plot on
a farm was using water sources other than rainfall, such as surface water resources, ground
water or stored water, in any season of the survey year, the plot was assumed to be irrigated.
Table 1 provides a breakdown of where irrigation is employed by country based on the
10
survey data. It is evident that irrigation plays a prominent role in Egypt and South Africa and
also in places such as Cameroon, Kenya and Zimbabwe.
Figure 2 depicts the mean net revenue for dryland and irrigated farms in each country in the
sample. Net revenue is gross revenue minus the costs of transport, packaging and marketing,
storage, post-harvest losses, hired labor (valued at the median market wage rate), light farm
tools (such as files, axes, machetes), heavy machinery (tractors, ploughs, threshers and
others), fertilizer and pesticide. The median prices per district were used to value both crops
and inputs whenever possible. In some circumstances, it was necessary to rely on median
provincial or national prices. We excluded household labor in the definition of net revenue
because including it led to many households having negative net revenues. This was the case
whether we used the payments each household alleged it gave household workers or whether
we assigned market wage rates to household labor. The inclusion of household labor in net
revenues is problematic, as reported in the agricultural development literature (Bardhan &
Udry 1999). We therefore defined net revenues without household labor costs and controlled
for the effect of household labor by including adult and child man-days as an independent
variable.
Table 4 presents the median net revenue in each country for irrigated and dryland farms. It is
evident from Figure 2 and Table 4 that Egypt is a unique case in Africa. Farming in Egypt is
predominantly irrigated and technology intensive, leading to significantly higher earnings. A
large proportion of Egyptian farmers are also able to cultivate for two seasons, which gives
them another advantage over dryland farmers in the rest of our sample.
Following the theoretical model described in Section 2, we estimated multiple regression
models of net revenue across three samples (see Table 6). This initial set of regressions does
not control for regional differences across Africa. The set examines three models: the entire
sample (all farms), just irrigated farms, and just dryland farms. The coefficients for irrigated
and dryland farms are not the same, suggesting they have different relationships with the
independent variables. While we do not present the results here, a number of farmer specific
variables, such as gender, education and whether or not the farmer was a full time farmer or
not, were not significant and so were dropped. Overall the three regressions explain 35%,
17% and 29% of the variation in net revenues from farm to farm. The coefficients of the
models are significantly different from zero. The variables identify many reasons why farm
net revenue varies from place to place. However, a great deal of the variation remains
11
unmeasured. This is especially true of dryland farms that vary from small backyard systems
to large commercial operations. There are several sources of possible error, including
misreporting of net revenue, omitted variables, local or national restrictions, and random
annual phenomena.
Many of the control variables were significant. More water flow increases the value of
irrigated farms but not dryland farms. Dryland farms are no better off with water flow
because the only water they use comes from on farm precipitation. Farm area reduces the
value per hectare of farms at a decreasing rate. That is, small farms are more productive on a
per hectare basis. Small farms may appear to be more productive because they are using a
fixed resource such as household labor over a much smaller piece of land. This is consistent
with the finding that the log of household size is positive in the all Africa and dryland
models. Higher elevation reduces the value of dryland farms but increases the value of
irrigated farms. In general, high elevation is associated with high diurnal temperature
variance, which is often hard on crops. However, high elevation may reduce the cost of
irrigation as the slopes can be used to capture and move water at low cost.
Technology variables also matter. Whether or not the farm has access to electricity has a
positive effect. This may reflect either higher technology or better access to markets. Whether
a farm has irrigation increases farm net revenue substantially. This dummy reflects the cost of
irrigation because irrigation costs are not subtracted from net revenue (they were not
measured in the survey).
Soils also were quite important in the model. Altogether 12 soil types were identified as
significant in the Africa sample. Types such as cambic arenosols (qc), rhodic ferralsols with
fine texture in hilly to steep regions (frFHS) and calcic yermosols with coarse to moderate
texture and in undulating to hilly regions (ykCMUH) were identified as high productivity
soils. By contrast, eutric gleysols with coarse texture in undulating areas (geCU), orthic
luvisols in moderate to hilly areas (loMH), chromic vertisols with fine texture in undulating
areas (vcFU), and chromic luvisols in moderate to steep areas (lcMS) were all particularly
unproductive soils. Some of these soil types were unique to small areas and so could not be
included in the dryland and irrigated equations.
The effects of the seasonal climate variables vary across the three models in Table 5. Both
linear and squared terms are significant in certain seasons, implying that climate has a
12
nonlinear effect on net revenues. It is not obvious from the coefficients in Table 5 how the
quadratic seasonal climate variables affect net revenue, because both linear and squared terms
play a role. One can see from the negative/positive sign of the quadratic term that the
relationship is hill-shaped/U-shaped. However, depending on what temperature is being
examined, the marginal impact of a climate variable could be either positive or negative.
In Table 6 we present an alternative specification of the model. We have added regional
variables to capture differences across broad regions and a few more technology variables.
The regional dummies suggest that West Africa and North Africa are more productive
relative to southern Africa. On the contrary, East Africa is less productive than southern
Africa. The results of the technology variables are mixed. The coefficient for whether a farm
uses heavy machinery is positive, which most likely reflects modern technology. However,
the coefficient for whether a farm depends on animal power is insignificant.
In order to interpret the climate coefficients, we calculated the marginal impacts of a change
in each climate variable. The marginal values depend on the regression equation that is being
used and the climate that is being evaluated. Table 7 displays the results of using the three
regressions from Table 5 and from Table 6. In each case the marginal effect of temperature
and precipitation is evaluated at the mean for each sample. For example, the marginal effect
of temperature on irrigated land is evaluated at the mean temperature of irrigated land and the
marginal impact of precipitation on dryland is evaluated at the mean precipitation for dryland.
Irrigated farms are located in cooler (19.7°C) and drier (38.3mm/mo) locations compared to
dryland farms (22.2°C and 74.1mm/mo). The marginal temperature results are almost
identical with or without regional dummies. However, the marginal precipitation results are
higher with the regional dummies. Looking at the total sample results with the regional
dummies, evaluated at the African mean climate (22.1°C and 61.5mm/mo), the marginal
temperature effect is -28.5°C and the marginal precipitation effect is 3.3mm/mo. The
marginal temperature effect for dryland farms is almost the same at -26.7°C. By contrast, the
marginal effect of a temperature increase on irrigated farms evaluated at their mean
temperature is positive 35.0°C. Warmer temperatures increase the net revenues of irrigated
farms because the mean temperature of irrigated farms is relatively cool and because
irrigation buffers net revenues from temperature effects. The marginal precipitation effects
for dryland and irrigated farms are more similar (3.8/mm/mo for irrigated farms and
2.7/mm/mo for dryland) because irrigated farms are located in such dry locations.
13
In addition to marginal effects, another important perspective to look at is the climate
elasticities (the percentage change in net revenues for a percentage change in climate).
Because the mean net revenue of irrigated cropland ($1367/ha) is much higher than the net
revenue from dryland cropland ($360/ha), the climate elasticities for irrigated land are
smaller. For example, the temperature elasticity for dryland is -1.9 but the elasticity for
irrigated land is +0.6. This reflects a dryland farm sensitivity to climate that is nearly three
times that of irrigated farms and in the opposite direction. The precipitation elasticity for
dryland is +0.6 but the elasticity for irrigated land is +0.1. The net revenues from irrigated
land are relatively less climate sensitive than those of dryland.
In order to provide a more complete sense of the climate response functions implied in Table
5, we plotted the net revenues of an average farm at different temperatures and rainfall levels.
Figures 3a and 3b illustrate the average results for the entire sample of farms (mixing
irrigated and dryland farms together). Figures 3a and 3b clearly show that net revenues
decline with temperature and rise with precipitation in Africa. The shape of the temperature
function, however, is worth noting. Results from Ricardian regressions estimated in the
United States (a temperate country) implied a hill-shaped function. Because of its hot initial
temperature, Africa lies on the right hand side of this hill, implying warming would have
large negative impacts (Mendelsohn et al. 1994; 1999; 2001). Although the results in Africa
are consistent with a hill-shaped model, they imply that the net revenues decline gently rather
than precipitously. Estimating the Ricardian model with African data reveals that there are
additional crops and methods suited to these higher temperatures which may not have been
used in a region with a temperate climate such as the US. It is also worth noting with regard
to Figure 3b that precipitation increases are generally beneficial to crops in Africa because it
is so dry to start with.
Figures 4a and 4b examine the response function of dryland alone. Most African farms use
dryland methods so the response function for dryland looks quite similar to the response
function for all of Africa. The temperature and precipitation functions are slightly steeper for
dryland than for all farms but the difference is not significant.
Figures 5a and 5b illustrate the temperature and precipitation response functions for irrigated
cropland. These response functions reveal that higher temperatures reduce net revenues per
hectare but at a rate of reduction that is lower than for dryland farms. Irrigated farms appear
14
to be more resilient to higher temperatures. The precipitation response function for these farm
types suggests (as expected) higher revenue per hectare with additional precipitation.
4. Forecasts of climate impacts
We used the estimated response functions to explore how climate change scenarios might
affect cropland in all of Africa. The Ricardian model estimates how climate affects net
revenue per hectare. In order to extrapolate from the sample to the entire continent, however,
it is necessary to know how many hectares of cropland there are in each district. In this paper,
we rely on estimates by the IFPRI (International Food Policy Research Institute) and FAO of
the amount of cropland in each district (Lotsch 2006, FAOStat 2005). The map of cropland is
shown in Figure 6. The primary arable land areas are in the temperate regions of North
Africa, the coastal belt in West Africa (south of the Sahel) and along the Rift Valley in
Eastern and Southern Africa.
Because we intended to explore the effects of climate on dryland and irrigated land, we
needed to determine which land across Africa is irrigated. We relied on FAO estimates of the
total hectares of irrigated cropland in each country (FAOStat 2005, Siebert et al. 2005). We
allocated these hectares across districts within each country on the basis of the districts'
respective climates. The probability of irrigation in each district was interpolated using a
probit model that regressed irrigation on a set of independent climate variables including
climate, soils and flow (the regression results can be requested from the authors). Figure 7
shows the irrigation results, which suggest that coastal regions in North Africa and southern
Africa have a higher likelihood of irrigation. Other regions of Africa, particularly central
Africa and regions along the Rift Valley, either have sufficient rainfall and/or lack the
investment necessary to undertake irrigation. Note that the estimate of the amount of cropland
and the percent of irrigation is based solely on current climate and is assumed not to change.
The question of whether cropland and irrigation are sensitive to climate is taken up in other
papers as part of this project.
Uniform scenarios
We began the analysis of the effect that climate change is likely to have on African farms,
ceteris paribus, by examining some uniform climate change scenarios. The uniform climate
scenarios provided a simple set of climate changes that allow one to see how the model
behaves and which components of climate are important.
15
Using the estimated regression coefficients in Table 5, we examined how changes in climate
change net revenue per hectare in each district throughout Africa (Equation 4). We then
multiplied the change in net revenue per hectare by the number of hectares of cropland in
each district to get an aggregate impact in each district. This value was then summed across
all the districts of Africa to get a total impact for a country or for the continent:
Aggregate climate impactd= Sum(Yi*Wj) (5)
where Yi = change in net revenue per hectare from a climate change
Wj =hectares of cropland, irrigated cropland or dryland cropland
d= district d
The results of the uniform climate scenarios are presented in Table 8. Four uniform climate
scenarios were tested: changes of +2.5°C, +5°C, -7% precipitation, and -14% precipitation.
The 2.5°C warming results in predicted losses of $23 billion for dryland, a gain of $1 billion
for irrigated cropland, and a loss of $16.4 billion for all African cropland. The separate
temperature impacts for dryland and irrigated cropland are greater than the impacts for the
total sample. The analysis of the total sample allows land to change between dryland and
irrigation as temperature rises, reducing the extent of the damage. Doubling warming to 5°C
increases the benefits to irrigation to $3.4 billion but the losses to dryland increase to $38
billion and all African cropland to $31 billion. Reducing precipitation reduces both dryland
and irrigated land net revenue about the same amount on a per hectare basis. Curiously,
reductions in precipitation are predicted to cause much larger losses in the total sample than
in the component parts.
Figure 8a depicts the geographic distribution of impacts from a uniform warming of 2.5°C.
Although the warming is assumed to be the same in every district, the impact depends on the
initial temperature of the district. Figure 8a shows that net revenues in districts in and near
the Sahara desert and in southern Africa fall the most with uniform warming. Districts across
the equator are much less affected (±$25/ha) relative to per hectare impacts in other regions.
Doubling warming to 5°C (Figure 8b) does not change the distribution of impacts across
16
Africa a great deal but it does increase the magnitude of the losses in districts that are
damaged. Reducing precipitation (Figure 8c) has a much larger harmful effect on the wetter
parts of Africa. The central humid band of the continent bears the brunt of the damage in this
scenario. Doubling the precipitation loss to 14% (Figure 8d) increases the magnitude of the
losses and their extent near the humid zone and equatorial Africa.
AOGCM scenarios
We also examined a set of climate change scenarios predicted by Atmospheric-Oceanic
Global Circulation Models (AOGCMs). We relied on three scenarios consistent with the
range of outcomes in the most recent IPCC (Intergovernmental Panel on Climate Change)
report (Houghton et al. 2001). Specifically, we used the A1 scenarios from the following
models: CCC (Canadian Climate Centre) (Boer et al. 2000), CCSR (Centre for Climate
System Research) (Emori et al. 1999), and PCM (Parallel Climate Model) (Washington et al.
2000). In each of these scenarios, climate changes at the grid cell level were summed to
predict climate changes by country. We then examined the consequences of these country
level climate change scenarios for 2020, 2060, and 2100.
For each climate scenario, we added the predicted change in temperature from the climate
model to the baseline temperature in each district. We also multiplied the predicted
percentage change in precipitation from the climate models by the baseline precipitation in
each district or province. This gave us a new climate for every district in Africa. Table 9
presents the mean temperature and rainfall predicted by the three models for the years 2020,
2060 and 2100. In Africa in 2100, PCM predicts a 2°C increase, CCSR a 4°C increase and
CCC a 6°C increase in temperature. Rainfall predictions are noisier: PCM predicts a 10%
increase in rainfall in Africa, CCC a 10% decrease, and CCSR a 30% decrease. Even though
the mean rainfall in Africa is predicted to increase/decrease depending on the scenario, there
is substantial variation in rainfall across countries.
Examining the path of climate change over time reveals that temperatures are predicted to
increase steadily until 2100 for all three models. Precipitation predictions, however, vary
across time for Africa: CCC predicts a declining trend; CCSR predicts an initial decrease, and
then increase, and decrease again; PCM predicts an initial increase, and then decrease, and
increase again. However, it should be noted that because the AOGCMs make geographically
detailed predictions the predicted changes for individual countries vary.
17
We applied the same methodology as outlined above for the uniform climate scenarios. We
first calculated the level of farm net revenue under current conditions and then the level of net
revenue under each climate scenario. The net revenues were predicted using the estimated
regression coefficients from Table 6. The change in net revenue was then multiplied by the
hectares of cropland in that district. These impacts were then summed across all districts in
Africa. We relied on the same underlying predictions of the quantity of cropland and
irrigation as with the uniform scenarios. Of course, with the AOGCM scenarios the predicted
change in net revenues will be different depending on the climate scenario.
In Table 10 we present the results of the nine scenarios: for the three models in three time
periods. The PCM results suggest that with ample rainfall and only a small increase in
temperature the net effect on all African farms would be a gain of from $87 to $97 billion per
year. The CCSR results suggest that substantial drying and warming together would generate
losses of from $19 to $27 billion beyond 2060 across Africa. The CCC results suggest that a
large warming of 6C would lead to substantial losses across African farms equal to $48
billion by 2100. Irrigated farms are predicted to benefit across all but one of these scenarios,
partly because they are climate insensitive and partly because they are located in relatively
cool areas. Dryland farms are likely to be affected the most, whether it is a benefit of $72
billion or a loss of $44 billion.
Figures 9 to 11 illustrate the distribution of impacts given that each country will face its own
climate scenario. Figures 9a, 9b, and 9c illustrate the impacts per hectare from the PCM
climate regimes for 2020, 2060, and 2100. According to the PCM model, climate changes
cause African net revenues to rise in all three time periods over the next century. Moderate
temperature changes and favorable precipitation generally increase African productivity
(although not necessarily as much as in other regions of the world). However, there are three
bands of land stretching from east to west across Africa where net revenues fall: along the
Mediterranean coast, from Kenya through West Africa, and across the southern tip of Africa.
These three bands of land experience moderate to high losses in all three time periods.
Figures 10a, 10b, and 10c illustrate the impacts according to the CCSR climate scenarios.
The 2020 CCSR scenarios are predicted to lead to significant losses per hectare in all of
northern, western and southwestern Africa. By contrast, farms in the central humid region
experience large benefits and farms in much of eastern and southeastern Africa experience
moderate benefits. In 2060, the CCSR scenario limits benefits strictly to the humid region of
18
Africa while the rest of Africa experiences drought conditions. In 2100, farms in many parts
of the continent continue to suffer from reduced rainfall except for the humid central region
and parts of eastern Africa. Niger, Mali, Somalia and other traditionally dry regions benefit
from this scenario, because CCSR predicts large increases in rainfall in these regions. The
beneficial impacts of the added rain in traditionally dry regions can outweigh the harmful
effects of higher temperatures. In the CCC scenarios (Figures 11a­c) much of Africa is
vulnerable to adverse impacts except for the West African coast, the humid center, and the
northeast. The damages in regions that are harmed get more severe over time with the CCC
scenario as temperatures are predicted to rise rapidly.
It is also helpful to remember where the people are living in order to judge which impacts
will be the most severe in human terms. Figure 12 provides a population density map of
Africa (CIESIN 2004). In conjunction with Figures 9, 10, and 11, one can see where climate
change may affect the most people. The population map indicates that West Africa (south of
the Sahel), the Mediterranean coastline, and a band across central Africa and a north to south
band in Eastern Africa are among the most densely populated. These areas coincide with
regions that the CCC and CCSR climate predictions suggest will be harmed. Even the
impacts from the relatively more favorable climate scenario based on the PCM models
suggest that populated regions such as the Mediterranean coastline, southern Africa and
central Africa will be severely affected. The results suggest that the impacts on rural
populations in Africa from climate change are likely to be significant.
5. Conclusions and implications for policy
This study is a cross-sectional analysis of the net revenues of African farms, relying on the
Ricardian method to investigate the impact of climate on net revenues, and building on a
massive data effort to collect information about farmers in 11 African countries. Surveys of
over 9000 African farms were combined with detailed measurements of soils, climate,
hydrology and elevation from a number of sources.
The study found that African farms are indeed sensitive to climate and especially
temperature. It predicts that farm values will decline if temperatures rise. Specifically, the
temperature elasticity with respect to net revenue of African farms is estimated to be -1.3.
The precipitation elasticity is estimated to be 0.4. The sensitivity is the greatest for dryland
farms with a temperature elasticity of -1.6 and a precipitation elasticity of 0.5. Irrigated
19
farms, by contrast, are resilient to temperature changes and may actually increase in value
(partly because of their location in temperate regions of Africa).
The study examined the impacts of future scenarios from climate models. Mild climate
scenarios predict future benefits across African cropland for irrigated and especially dryland
farms. Yet even in these favorable scenarios regions in the Mediterranean, central, western
and southern Africa that are currently productive stand to be adversely affected. More
dramatic warming scenarios predict small damages by 2020 that increase steadily over time.
These damages are exacerbated by drying. For example, the CCSR scenario predicts extreme
drying that leads to damages of $26 billion for dryland by 2060. For Africa as a whole, the
harmful scenarios predict losses from $27 to $48 billion by the end of the century, whereas
the beneficial scenario predicts gains of $97 billion. There is a wide range of possible
outcomes across these plausible climate scenarios.
The study found that impacts are not likely to be uniform across Africa. The hotter and drier
regions of Africa are likely to be hurt the most. The distribution of impacts across Africa also
depends on the climate scenario. Finally, how many people are affected depends on where
they are located. Putting all these factors together, it is hard to predict what actually will
happen in Africa. There remains a wide range of plausible outcomes. It is even more difficult
to predict what will happen in specific places. However, it is evident from this study that
Africa will be affected by climate change.
The study suggests that African countries should begin to plan for climate contingencies.
Governments should begin to develop contingency plans if certain climate outcomes come to
pass. They should anticipate what farmers will do, how markets will react, and what role
governments need to play. Governments should be prepared to help people adapt to these
new circumstances.
Some actions can also be taken before climate changes. Actions that make agriculture sectors
more immune to climate can be taken in advance. Developing new crops and livestock that
are more suited to hot and dry conditions will help countries adapt to many current climate
zones as well as future ones. Developing profitable irrigated agriculture systems will reduce
the climate vulnerability of the agriculture sector. Developing the economy away from
agriculture will reduce the climate sensitivity of the entire economy. Increasing wealth so that
firms and households can explore more alternatives will make adaptation easier.
20
The impacts of warming on African agriculture are significant. This study confirms what
scientists have long suspected. Dryland farmers in Africa will be vulnerable to increases in
temperature. Although farmers have some adaptations available to them, such as switching to
more heat tolerant crops, if they continue with their current technology warming will have a
devastating effect on dryland farmers. African agriculture appears to be extremely vulnerable
to climate change. However, not all of Africa is likely to experience the same effects from it.
The humid regions of Africa are likely to be less vulnerable to warming than the drier
northern and southern regions. Exactly how climate change will affect individual countries
varies a great deal across climate models.
There are two important factors that must be considered that were not included in this
analysis. First, this study takes the technology of each farmer as given. There is no doubt
about the importance of technology. The average dryland farmer earns $319/ha whereas the
average irrigated farmer earns $1261/ha. The more advanced irrigated farms earn even more.
What will happen to technology in Africa's future is very important. The second important
factor left out of this analysis is carbon fertilization. Experimental results suggest that yields
could increase on average by 30% if CO2 doubles (Reilly et al. 1996). If these gains are
realized in the field, they will help to offset a great deal of the otherwise inevitable harmful
effects of warming.
21
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24
Table 1: Useable surveys by country
Country Dryland Irrigated Total
Burkina Faso 990 41 1031
Cameroon 646 105 751
Egypt 0 802 802
Ethiopia 874 66 940
Ghana 849 29 878
Kenya 675 79 754
Niger 849 48 897
Senegal 1037 31 1068
South Africa 199 87 286
Zambia 956 14 970
Zimbabwe 597 90 687
Total 7672 1392 9064
25
Table 2: Temperature (°C) normals (Sample means)
Country Winter Spring Summer Fall
Burkina Faso 23.6 28.3 28.9 24.5
Cameroon 19.4 21.4 20.0 18.9
Egypt 11.7 13.2 24.1 23.4
Ethiopia 18.6 21.5 19.7 18.1
Ghana 21.8 24.8 22.6 21.2
Kenya 18.8 19.7 18.4 19.1
Niger 26.3 30.8 33.9 29.2
Senegal 24.5 29.1 31.5 26.7
South Africa 11.5 15.5 20.7 19.4
Zambia 16.7 21.7 21.1 19.6
Zimbabwe 16.6 21.3 22.5 20.6
Africa-wide 19.8 23.4 24.5 22.2
Note: Seasonal climates have been adjusted so that they are consistent regardless of hemisphere.
26
Table 3: Precipitation (mm/mo) normals (Sample means)
Country Winter Spring Summer Fall
Burkina Faso 2.6 15.8 113.8 133.1
Cameroon 60.3 101.9 185.1 228.6
Egypt 12.8 7.0 2.3 3.5
Ethiopia 19.4 49.2 123.7 117.5
Ghana 30.9 59.7 112.4 111.7
Kenya 88.4 103.0 84.3 60.0
Niger 0.8 3.2 64.1 70.6
Senegal 2.2 1.1 47.9 112.7
South Africa 1.8 55.0 86.4 68.8
Zambia 48.3 57.7 108.6 100.7
Zimbabwe 7.5 15.4 138.8 90.0
Africa-wide 25.9 39.8 96.1 102.4
Note: Seasonal climates have been adjusted so that they are consistent regardless of hemisphere.
27
Table 4: Net revenues per ha (in US$)
Country Total Dryland Irrigated
Burkina Faso 328 318 538
Cameroon 987 952 1217
Egypt 1660 1660
Ethiopia 199 188 345
Ghana 422 419 496
Kenya 267 255 365
Niger 125 119 227
Senegal 239 237 282
South Africa 811 538 1445
Zambia 134 133 145
Zimbabwe 432 403 643
Average per ha 462 319 1261
28
Table 5: Regression coefficients of all farms, dryland farms and irrigated farms
without regional dummies
Variable All farms Dryland Irrigated
Winter temperature -83.9 -117.1* 91.0
Winter temp squared 2.98* 3.62* -2.16
Spring temp -18.4 -20.9 -186.3
Spring temp sq -1.61 -1.10 2.21
Summer temp 212.4** 118.9 1093.0**
Summer temp sq -2.74** -1.36 -19.01**
Fall temp -116.6* -22.8 -1067.4**
Fall temp sq 1.68 -0.23 22.28**
Winter precipitation -3.32** -4.79** 7.86
Winter prec sq 0.018** 0.025** -0.043
Spring prec 3.42* 5.38** -11.99
Spring prec sq -0.002 -0.017** 0.099*
Summer prec 3.90** 3.43** 23.84**
Summer prec sq -0.016** -0.015** -0.093**
Fall prec -1.63* -1.76** -19.82**
Fall prec sq 0.012** 0.013** 0.074**
Mean flow 12.20** -8.48* 10.54**
Farm area -0.074** -0.320** -0.042*
Farm area sq 0.000** 0.000** 0.000*
Elevation -0.077** -0.115** 0.234*
Log (household size) 27.3* 20.93 64.5
Irrigate (1/0) 251.3**
29
Table 5 (continued):
Variable All farms Dryland Irrigated
Household access to
electricity (1/0) 117.4** 95.47** 297.8**
Soil (geCU) -692.4** -393.3** -1265.7**
Soil (ilqHS) -454.4** -228.1** -1038.0**
Soil (loMH) -2322.0** -1999.8**
Soil (vcFU) -1065.1** -894.3** -1585.5**
Soil (lcMFU) -261.2** -250.2**
Soil (qc) 1642.8** 1709.0**
Soil (ql) -539.9** -269.6**
Soil (lcMS) -2267.6 -5812.3**
Soil (nd) 370.7 7343.7**
Soil (lg) -179.0** -125.2**
Soil (frFHS) 992.4* 3540.0
Soil (ykCMUH) 1279.6** -636.3**
Constant 141.8 702.4 -243.3
N 8459 7238 1221
R2 0.351 0.171 0.29
F 68.59 33.81 52.45
Notes: * significant at 5% level ** significant at 1% level
Soil definitions: Rhodic ferralsols with fine texture in hilly to steep areas (frFHS), eutric gleysols with coarse
texture in undulating areas (geCU), lithosols hilly to steep slope (ilqHS), chromic luvisols with medium to fine
texture in undulating areas (lcMFU), chromic luvisols in moderate to steep areas (lcMS), gleyic luvisols (lg),
orthic luvisols in moderate to hilly areas (loMH), dystric nitrosols (nd), cambic arenosols (qc), luvic arenosols
(ql), chromic vertisols with fine texture in undulating areas (vcFU), calcic yermosols with coarse to moderate
texture and in undulating to hilly areas (ykCMUH), lithosols in hilly and steep areas (ilqHS), luvic arenosols
(ql), dystric nitosols (nd), gleyic luvisols (lg).
30
Table 6: Regression coefficients of all farms, dryland farms and irrigated farms
with regional dummies
Variable All farms Dryland Irrigated
Winter temperature -173.6** -106.7 -93.5
Winter temp squared 6.1** 3.9* 4.9
Spring temp 115.1 -82.8 58.7
Spring temp sq -5.0** -0.3 -4.1
Summer temp 173.9** 198.6** 827.5**
Summer temp sq -1.9 -3.2* -13.1*
Fall temp -98.1 -92.4 -824.2*
Fall temp sq 1.1 1.5 15.3*
Winter precipitation -2.9* -1.9 5.8
Winter prec sq 0.0** 0.00 0.00
Spring prec 3.5* 3.6** -10.6
Spring prec sq -0.001 -0.011* 0.091*
Summer prec 3.4** 1.9* 21.4**
Summer prec sq -0.012** -0.005 -0.086**
Fall prec -0.5 -0.6 -14.7**
Fall prec sq 0.0055* 0.0053* 0.0586***
Mean flow 9.4** -5.4 8.8**
Farm area -0.1** -0.3** -0.0**
Farm area sq 0.0* 0.0** 0.0*
Elevation 0.035 -0.0009 0.229
Log (household size) 22.9 10.1 62.4
Irrigate (1/0) 237.5**
Household access to
electricity (1/0) 66.6** 47.7** 233.2*
31
Table 6 (continued):
Variable All farms Dryland Irrigated
Soil (geCU) -631** -287** -540
Soil (ilqHS) -387** -156** -1147**
Soil (loMH) -2181** -1959**
Soil (vcFU) -1180** -1006** -1719**
Soil (lcMFU) -295** -241**
Soil (qc) 1633** 1726**
Soil (ql) -482** -188**
Soil (lcMS) -2153 -6157**
Soil (nd) 214 7051**
Soil (lg) -199** -154**
Soil (frFHS) 1428** 3212
Soil (ykCMUH) 1071** 148
West Africa dummy 136** 208** -285
North Africa dummy 457** 675*
East Africa dummy -186** -154** -361
Heavy machinery dummy 51.8** 55.5** -60.8
Animal power dummy 10.4 49.3** -185.5**
Constant -388 1081 -549
N 8459 7238 1221
R2 0.4 0.2 0.3
F 63.6 32.4 46.3
Notes: * significant at 5% level ** significant at 1% level
Soil definitions: Rhodic ferralsols with fine texture in hilly to steep areas (frFHS), eutric gleysols with coarse
texture in undulating areas (geCU), lithosols hilly to steep slope (ilqHS), chromic luvisols with medium to fine
texture in undulating areas (lcMFU), chromic luvisols in moderate to steep areas (lcMS), gleyic luvisols (lg),
orthic luvisols in moderate to hilly areas (loMH), dystric nitrosols (nd), cambic arenosols (qc), luvic arenosols
(ql), chromic vertisols with fine texture in undulating areas (vcFU), calcic yermosols with coarse to moderate
texture and in undulating to hilly areas (ykCMUH), lithosols in hilly and steep areas (ilqHS), luvic arenosols
(ql), dystric nitosols (nd), gleyic luvisols (lg).
32
Table 7: Marginal impacts of climate on net revenue (US$/ha)
(Evaluated at the mean of the Africa, irrigated and dryland sample)
Without regional dummies (From coefficients in Table 5)
Sample Africa Irrigated Dryland
regression regression regression
Temperature -28.3** 33.6 -23.0**
(-1.3) (0.5) (-1.6)
Precipitation 2.65** 2.08 2.02**
(0.36) (0.06) (0.47)
With regional dummies (From coefficients in Table 6)
Annual Africa Irrigated Dryland
regression regression regression
Temperature -28.5** 35.04 -26.7**
(-1.4) (0.6) (-1.9)
Precipitation 3.28** 3.82 2.7**
(0.44) (0.13) (0.63)
** significant at 1% level
33
Table 8: Africa-wide impacts from uniform climate scenarios
Impacts 2.5°C 5°C 7% 14%
warming warming decreased decreased
precipitation precipitation
Dryland
Net revenue -72.2 -120.4 -14.1 -28.3
($ per ha) (-16%) (-30%) (-6%) (-11%)
Total net revenue
-22.6 -37.7 -4.4 -8.9
(billions $)
Irrigated
Net revenue 110.3 258.8 -15.9 -31.5
($ per ha) (9%) (23%) (-1.4%) (-2.7%)
Total net revenue
1.4 3.4 -.21 -0.41
(billions $)
Total (Africa)
Net revenue -49.2 -95.7 -18.3 -37.2
($ per ha) (-11.3%) (-21.9%) (-4.2%) (-8,5%)
Total net revenue
-16.0 -31.2 -5.96 -12.1
(billions $)
Note: Using coefficients in Table 6 and changes to climate that are uniform across Africa. The numbers in
brackets represent the percentage change in net revenue per hectare relative to the mean of the sample.
34
Table 9: Climate predictions of AOGCM models for 2020, 2060 and 2100
Model Current 2020 2060 2100
CCC 23.29 24.94 26.85 29.96
CCSR Temperature 23.29 25.27 26.17 27.39
PCM 23.29 23.95 24.94 25.79
CCC 79.75 76.84 71.86 65.08
CCSR Precipitation 79.75 73.99 76.67 62.44
PCM 79.75 89.58 80.72 83.18
35
Table 10: Africa-wide impacts from AOGCM climate scenarios
PCM PCM PCM CCSR CCSR CCSR CCC CCC CCC
Impacts
2020 2060 2100 2020 2060 2100 2020 2060 2100
Dryland
Net Revenue 231.6 196.2 199.7 -12.8 -82.8 -128.9 -72.1 -92.1 -139.0
($ per ha) (73.3%) (62.1%) (63.2%) (-4%) (-26%) (-40%) (-22%) (-29.2) (-44%)
Total Net
Revenue 72.4 61.4 62.5 -4.0 -25.9 -40.3 -22.5 -28.8 -43.5
(billions $)
Irrigated
Net Revenue 468.9 506.5 586.8 76.6 142.3 -420.9 49.1 137.6 297.1
($ per ha) (40%) (44%) (51%) (6.7%) (12%) (-36%) (4.3%) (12%) (26%)
Total Net
Revenue 6.1 6.6 7.6 .99 1.8 -5.5 0.6 1.78 3.9
(billions $)
Total (Africa)
Net Revenue 277.8 268.2 296.8 38.7 -58.7 -82.7 -71.1 -72.6 -148.7
($ per ha) (63%) (61.5% (68%) (9%) (-13%) (-19%) (-16%) (-17%) (-34%)
Total Net
Revenue 90.5 87.4 96.7 12.6 -19.1 -26.9 -23.2 -23.6 -48.4
(billions $)
Note: Using coefficients in Table 6 and AOGCM country specific climate scenarios. The numbers in brackets
represent the percentage change in net revenue per hectare relative to the mean of the sample.
36
100
90
80
70
60
50
40
30
20
10
0
BFAS EGY ETH GHA NIG SEN SAF ZAM CAM KEN ZIM
Variation of Flow and Runoff Mean
Mean Flow (million m3) Mean Runoff (mm)
Source: IWMI/University of Colorado
Figure 1: Mean flow (m3) and runoff (mm)
37
Figure 1b: Long run seasonal mean temperature (°C)
38
Figure 1c: Long run seasonal mean precipitation (mm/mo)
39
Figure 1d: Elevation (m)
40
2,000
1,500
1,000
500
0
BFAS EGY ETH GHA NIG SEN SAF ZAM CAM KEN ZIM
Dryland Irrigated
Note: Net revenue = Gross revenue ­ less total cost of hired labor, small tools and heavy machinery, fertilizer
and pesticide
Figure 2: Net revenue per hectare of dryland and irrigated cropland by country ($/ha)
41
500
ha
$/SU 400
e
nuevert 300
ne
m
arf
edtic 200
edrP
100
20 25 30 35 40
Temperature (Celsius)
Figure 3a: Temperature response function ­ All farms in Africa
1500
ha
$/SU
e 1000
nuevert
ne
m
arf
edtic 500
edrP
0
0 100 200 300 400 500
Precipitation (mm)
Figure 3b: Precipitation response function ­ All farms in Africa
42
400
ha
$/SU 350
e
nuevert 300
ne
m
arf 250
edtic
edrP 200
150
20 25 30 35 40
Temperature (Celsius)
Figure 4a: Dryland farm temperature response function
1000
ha
$/SU 800
e
nuevert 600
ne
m
arf
edtic 400
edrP
200
0 100 200 300 400
Precipitation (mm)
Figure 4b: Dryland farm precipitation response function
43
1100
ha
$/SU 1000
e
nuevert 900
ne
m
arf 800
edtic
edrP 700
600
20 22 24 26 28 30
Temperature (Celsius)
Figure 5a: Irrigated farm temperature response function
3000
ha
$/SU 2500
e
nuevert 2000
ne
m
arf
edtic 1500
edrP
1000
100 200 300 400
Precipitation (mm)
Figure 5b: Irrigated farm precipitation response function
44
Note: Provincial borders are shown so that the variation in percentages can be seen clearly.
Figure 6: Estimated fraction of cropland in district (Based on IFPRI data)
45
Note: Provincial borders are shown so that the variation in percentages can be seen clearly.
Figure 7: Estimated fraction of irrigation by location (Based on FAO data)
46
Figure 8a: Change in net revenue per hectare from 2.5°C warming
47
Figure 8b: Change in net revenue per hectare from 5°C warming
48
Figure 8c: Change in net revenue per hectare from 7% decrease in precipitation
49
Figure 8d: Change in net revenue per hectare from 14% decrease in precipitation
50
Figure 9a: Change in net revenue per hectare from PCM climate scenario in 2020
51
Figure 9b: Change in net revenue per hectare from PCM climate scenario in 2060
52
Figure 9c: Change in net revenue per hectare from PCM climate scenario in 2100
53
Figure 10a: Change in net revenue per hectare from CCSR climate scenario in 2020
54
Figure 10b: Change in net revenue per hectare from CCSR climate scenario in 2060
55
Figure 10c: Change in net revenue per hectare from CCSR climate scenario in 2100
56
Figure 11a: Change in net revenue per hectare from CCC climate scenario in 2020
57
Figure 11b: Change in net revenue per hectare from CCC climate scenario in 2060
58
Figure 11c: Change in net revenue per hectare from CCC climate scenario in 2100
59
\
Source: Center for International Earth Science Information Network (CIESIN)
Figure 12: Population density map (2000)
60